In [1]:
import pandas as pd
import seaborn as sns
import plotly.express as px

import matplotlib.pyplot as plt
In [2]:
import plotly.io as pio
pio.renderers.default = "plotly_mimetype+notebook"

Matplotlib¶

For this excercise, we have written the following code to load the stock dataset built into plotly express.

In [3]:
stocks = px.data.stocks()
stocks.head()
Out[3]:
date GOOG AAPL AMZN FB NFLX MSFT
0 2018-01-01 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
1 2018-01-08 1.018172 1.011943 1.061881 0.959968 1.053526 1.015988
2 2018-01-15 1.032008 1.019771 1.053240 0.970243 1.049860 1.020524
3 2018-01-22 1.066783 0.980057 1.140676 1.016858 1.307681 1.066561
4 2018-01-29 1.008773 0.917143 1.163374 1.018357 1.273537 1.040708

Question 1:¶

Select a stock and create a suitable plot for it. Make sure the plot is readable with relevant information, such as date, values.

In [4]:
# Plot Netflix stock
plt.plot(stocks.date.values, stocks.NFLX.values)

# Make plot on x-axis readable
plt.xticks(rotation='vertical')
plt.xticks(range(0, stocks.shape[0], 10))

# Add titles and labels
plt.title('Netflix stock')
plt.xlabel('Date')
plt.ylabel('Stock value')

plt.show()

Question 2:¶

You've already plot data from one stock. It is possible to plot multiples of them to support comparison.
To highlight different lines, customise line styles, markers, colors and include a legend to the plot.

In [5]:
# Plot more than one stock (not all)
plt.plot(stocks.date.values, stocks.GOOG.values, label = "Google", linestyle="-")
plt.plot(stocks.date.values, stocks.NFLX.values, label = "Netflix", linestyle="--")
plt.plot(stocks.date.values, stocks.AMZN.values, label = "Amazon", linestyle="-.")
plt.plot(stocks.date.values, stocks.FB.values, label = "Facebook", linestyle=":")

# Make plot on x-axis readable
plt.xticks(rotation='vertical')
plt.xticks(range(0, stocks.shape[0], 10))

# Add titles, labels and legend
plt.title('Netflix stock')
plt.xlabel('Date')
plt.ylabel('Stock value')
plt.legend()

plt.show()

Seaborn¶

First, load the tips dataset

In [6]:
tips = sns.load_dataset('tips')
tips.head()
Out[6]:
total_bill tip sex smoker day time size
0 16.99 1.01 Female No Sun Dinner 2
1 10.34 1.66 Male No Sun Dinner 3
2 21.01 3.50 Male No Sun Dinner 3
3 23.68 3.31 Male No Sun Dinner 2
4 24.59 3.61 Female No Sun Dinner 4

Question 3:¶

Let's explore this dataset. Pose a question and create a plot that support drawing answers for your question.

Some possible questions:

  • Are there differences between male and female when it comes to giving tips?
  • What attribute correlate the most with tip?
In [7]:
g = sns.FacetGrid(tips, col='sex', hue='smoker')
g.map(sns.scatterplot, 'total_bill', 'tip')
g.add_legend()

plt.show()
In [8]:
sns.jointplot(x='total_bill', y='tip', data=tips)
plt.show()
In [9]:
sns.scatterplot(x='total_bill', y='tip', data=tips, hue='sex').set(title='Tips male vs. female')
plt.show()

Plotly Express¶

Question 4:¶

Redo the above exercises (challenges 2 & 3) with plotly express. Create diagrams which you can interact with.

The stocks dataset¶

Hints:

  • Turn stocks dataframe into a structure that can be picked up easily with plotly express
In [10]:
df_stocks = stocks.melt(id_vars=['date'], var_name='company')
df_stocks.head()
Out[10]:
date company value
0 2018-01-01 GOOG 1.000000
1 2018-01-08 GOOG 1.018172
2 2018-01-15 GOOG 1.032008
3 2018-01-22 GOOG 1.066783
4 2018-01-29 GOOG 1.008773
In [11]:
px.line(df_stocks, 'date', 'value', color='company', symbol='company')
Jan 2018Apr 2018Jul 2018Oct 2018Jan 2019Apr 2019Jul 2019Oct 2019Jan 20200.60.811.21.41.61.82
companyGOOGAAPLAMZNFBNFLXMSFTdatevalue
plotly-logomark

The tips dataset¶

In [12]:
fig = px.scatter(tips, x='total_bill', y='tip', color='sex', facet_col='smoker', 
                 facet_row='time')
fig.show()
10203040502468101020304050246810
sexFemaleMaletotal_billtotal_billtiptipsmoker=Nosmoker=Yestime=Lunchtime=Dinner
plotly-logomark

Question 5:¶

Recreate the barplot below that shows the population of different continents for the year 2007.

Hints:

  • Extract the 2007 year data from the dataframe. You have to process the data accordingly
  • use plotly bar
  • Add different colors for different continents
  • Sort the order of the continent for the visualisation. Use axis layout setting
  • Add text to each bar that represents the population
In [13]:
#load data
df = px.data.gapminder()
df.head()
Out[13]:
country continent year lifeExp pop gdpPercap iso_alpha iso_num
0 Afghanistan Asia 1952 28.801 8425333 779.445314 AFG 4
1 Afghanistan Asia 1957 30.332 9240934 820.853030 AFG 4
2 Afghanistan Asia 1962 31.997 10267083 853.100710 AFG 4
3 Afghanistan Asia 1967 34.020 11537966 836.197138 AFG 4
4 Afghanistan Asia 1972 36.088 13079460 739.981106 AFG 4
In [14]:
df_2007 = df.query('year==2007')
df_2007_new = df_2007.groupby('continent').sum()
fig = px.bar(df_2007_new, x='pop', y=df_2007_new.index, color=df_2007_new.index, 
             text='pop', title="Population of different continents for the year 2007", 
             text_auto='.2s')
fig.update_layout(yaxis={'categoryorder':'total descending'})
fig.update_traces(textposition='outside')

fig.show()
930M900M3.8G590M25M00.5B1B1.5B2B2.5B3B3.5B4BAsiaAfricaAmericasEuropeOceania
continentAfricaAmericasAsiaEuropeOceaniaPopulation of different continents for the year 2007popcontinent
plotly-logomark